code: def load_model_from_checkpoint(
      args,
      n_entities,
      n_relations,
      ckpt_path,
      ent_feat_dim,
      rel_feat_dim,
  ):
      model = load_model(
          args, n_entities, n_relations, ent_feat_dim, rel_feat_dim
      )
      model.load_emb(ckpt_path, args.dataset)
      return model
  
imports: import torch.multiprocessing
  from torch.utils.data import DataLoader
  import torch.optim
  import torch
  from distutils.version import LooseVersion
  from .models.pytorch.tensor_models import thread_wrapped_func
  from .models import KEModel
  from .utils import save_model, get_compatible_batch_size
  import os
  import logging
  import time
  from functools import wraps
  import dgl
  from dgl.contrib import KVClient
  import dgl.backend
  from .dataloader import EvalDataset
  from .dataloader import get_dataset
  import pdb
  from collections import defaultdict
  from ogb.lsc import WikiKG90MDataset, WikiKG90MEvaluator
  from tqdm.auto import tqdm
  import pickle
  from math import ceil
label_desc: Use image node_id 2 for calling a global function with example usage: load_model_from_checkpoint(args, n_entities, n_relations, ckpt_path, ent_feat_dim, rel_feat_dim) and returns: model